In this work, the density fitting (DF) approximation is added to the restricted Hartree-Fock (RHF) implementation in the JuliaChem computational chemistry code. Utilizing a DF algorithm that uses symmetry and integral screening, a significant reduction in time to compute the Fock matrix is achieved. The symmetry and screening DF-RHF techniques were adapted to be performed on graphics processing units (GPUs), which are well suited to perform the matrix multiplications that comprise the bulk of the Fock build time in DF-RHF. The JuliaChem DF-RHF GPU algorithm employs a novel approach that automatically switches between two DF-RHF algorithms depending on the number of basis functions in the calculation. The JuliaChem GPU DF-RHF implementation demonstrates up to 2× speedup for Fock build times compared to the existing best-in-class GPU DF-RHF implementation by operating directly on screened intermediate matrices. Due to the high portability of the Julia language code, the JuliaChem CPU and GPU DF-RHF implementations could be benchmarked on a variety of CPU and GPU architectures from multiple hardware vendors.
In this work, the density fitting (DF) approximation is added to the restricted Hartree-Fock (RHF) implementation in the JuliaChem computational chemistry code. Utilizing a DF algorithm that uses symmetry and integral screening, a significant reduction in time to compute the Fock matrix is achieved. The symmetry and screening DF-RHF techniques were adapted to be performed on graphics processing units (GPUs), which are well suited to perform the matrix multiplications that comprise the bulk of the Fock build time in DF-RHF. The JuliaChem DF-RHF GPU algorithm employs a novel approach that automatically switches between two DF-RHF algorithms depending on the number of basis functions in the calculation. The JuliaChem GPU DF-RHF implementation demonstrates up to 2× speedup for Fock build times compared to the existing best-in-class GPU DF-RHF implementation by operating directly on screened intermediate matrices. Due to the high portability of the Julia language code, the JuliaChem CPU and GPU DF-RHF implementations could be benchmarked on a variety of CPU and GPU architectures from multiple hardware vendors.
The first chapter in this thesis describes the theoretical framework that underlies much of electronic structure theory. The second chapter provides a review of the modern state of the intersection between electronic structure theory and high-performance computing. The next four chapters describe the development and implementation of new code into the GAMESS software package that utilizes Graphics Processing Units (GPUs) to achieve optimal performance. Chapter Three discusses an initial implementation of the GPU-driven Fock build step for the Restricted Hartree-Fock (RHF) method and the subsequent performance analysis on the Summit supercomputer. Chapter Four describes further developments to the aforementioned GPU-driven RHF Fock build code. Chapter Five discusses the extension of the GPU-driven RHF Fock build algorithm to the entirety of the Self-Consistent Field (SCF) process, which enables multiple steps of the SCF process to utilize GPUs. Chapter Six discusses the development of a new, GPU-driven algorithm for the Møller-Plesset Second-Order Perturbation Theory method (MP2) using the Resolution of the Identity (RI) and a subsequent performance analysis of the new RI-MP2 algorithm on the Summit Supercomputer. The next two chapters discuss the development of JuliaChem, a new electronic structure theory software package implemented in the Julia programming language. Chapter Seven provides an initial performance assessment of the RHF SCF algorithm implemented in Julia compared to GAMESS. Chapter Eight describes further algorithmic developments to the JuliaChem RHF algorithm and provides a performance analysis compared to modern developments in the GAMESS code. Chapter Nine provides an analysis of the ethylenedione molecule and its inherently unstable nature using multiple theoretical methods. Chapter Ten, the final chapter of the dissertation, provides final concluding thoughts.
High Resolution Image Download MS PowerPoint Slide We introduce the first version of GPU4PySCF, a module that provides GPU acceleration of methods in PySCF . As a core functionality, this provides a GPU implementation of two-electron repulsion integrals (ERIs) for contracted basis sets comprising up to g functions using the Rys quadrature. As an illustration of how this can accelerate a quantum chemistry workflow, we describe how to use the ERIs efficiently in the integral-direct Hartree–Fock build and nuclear gradient construction. Benchmark calculations show a significant speedup of 2 orders of magnitude with respect to the multithreaded CPU Hartree–Fock code of PySCF and the performance comparable to other open-source GPU-accelerated quantum chemical packages, including GAMESS and QUICK, on a single NVIDIA A100 GPU.
GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to 52× over PySCF for SCF, 45× for MP2 on molecules with up to 470 basis functions, and 44× for FCI, while outperforming GPU4PySCF by up to 34× for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license.
In the field of electronic structure theory, many optimizations, ranging from accelerator offloading to exploitation of modern programming constructs, have been used to improve the performance of quantum chemistry software. However, one area that has remained largely unexplored is the space of novel programming languages that can provide unique benefits to quantum chemistry software development. One such programming language is Julia, an interpreted language designed to achieve the performance of high-performance, statically compiled languages. To assess the viability of Julia, the restricted Hartree-Fock method was implemented in a new quantum chemistry software package, JuliaChem. JuliaChem is a novel quantum chemistry software designed to take advantage of Julia as much as possible while achieving runtimes comparable to that of more established quantum chemistry software packages. Performance benchmarks against the popular quantum chemistry software package GAMESS, using the S22 test set of molecules, indicate that JuliaChem displays performance that is competitive with that of GAMESS. This shows that Julia could be a potentially useful tool for the field of electronic structure theory.
We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.
We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.